NHL Defensemen and Shooting Contributions back to 1967-68

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , , , , , , , Leave a comment

File:Defenseman Ray Bourque 1979.jpg

Photo by Dave Stanley via Wikimedia Commons

I have kicked around this data in the past, most prominently in my theoretical post on offensive systems, but I really wanted to get further into the intricacies of defensemen and their historical place in team shooting (among other offensive contributions). By looking at how much a defenseman contributes to a team’s shot generation (expressed as a percentage of team shots in the games a player played, or %TSh), we can draw some interesting comparisons across NHL eras, but I haven’t yet explored how the role of the defenseman has (or hasn’t) evolved from the Expansion Era to the present, nor have I taken a look at some of the more exceptional defense shooting teams. Let me correct that now.

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Wayne Gretzky vs. Bobby Clarke, December 1981: A Micro-Analysis

Benjamin Wendorf NHL Topical Analysis, Theory Tags: , , , , , , , , , 8 Comments

Image

Left image by “Centpacrr“, Right image by “Hakandahlstrom” via Wikimedia Commons, both altered by author

On December 30th, 1981, Wayne Gretzky’s Edmonton Oilers and Bobby Clarke’s Philadelphia Flyers met in a Wednesday night tilt rich with symbolism. Clarke, 32, was a couple of years away from retirement; two of his three remaining teammates from the Cup years, Reggie Leach and Bill Barber (defenseman Jimmy Watson was the third), were themselves out of the league in two years (Leach due to talent drop-off, Barber due to injury). Ironically, there was little indication in 1981 that this was going to happen – all were around 30, all were near point-per-game scorers playing all minutes. Whatever the case, they were the last of the Broad Street Bullies, and were now mentoring a new generation of “Bullies” like Ken Linseman, Tim Kerr, and Brian Propp, who seemed at times more annoying than dangerous. Though in transition, Philadelphia was still a great possession team (4th in the league in 2pS%, an historical possession metric), but fought the percentages all year to squeak into the playoffs. Edmonton, on the other hand, was romping through the league at record pace, and by December 30th held a comfortable lead over 2nd place Minnesota in the old Campbell Conference. Gretzky, of course, was at the heart of this surge, and by game 39 he had 45 goals.

The 1980s Oilers were the next step in NHL offense, really a Canadian version of the 1970s Soviet style of hockey. They didn’t need to bully their way to victories – they let the other team take the penalties, and skated all over them. I should say, that’s what Edmonton would eventually do; on this night they lined Gretzky up with Dave Lumley and Dave Semenko, as they had done most of the year. More on that later.

As I said before, though, the Flyers were a great possession team, as they always had been when Clarke and Barber were in their prime (they averaged, averaged, 55% 2pS% in the years 1973-74 through 1981-82, placing them consistently among the top 5 in the NHL). They were fast and calculating with their puck movement; the grit was just extra work – and who knows, maybe it contributed to Clarke, Barber, and Leach’s early retirement. The Bully when met with the Oilers, though, learned that the box was the bigger enemy.

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Friday Quick Graphs: When did “Score Effects” Emerge in NHL History?

Benjamin Wendorf Friday Quick Graphs, NHL League-Wide Analysis Tags: , , , , , , 7 Comments

Back in 2009, Tyler Dellow first elaborated on the idea of what we now call “score effects,” or how teams with a lead will go into a “defensive shell” and purposely withdraw from the possession battle to preserve their score. Score effects are the primary reason the go-to possession stat is “Fenwick Close” today – the “close” implies the importance of looking at possession measures when teams still have a reason to engage. The limits of historical shot recording, and the possibility of score effects, are precisely why I’ve advocated the use of 2pS% (shot-differential percentage from the first two periods) as an historical possession measure.

The one thing I never completely took for granted was that score effects had always existed in the NHL. To test this, I broke down each game into individual period shot battles, and looked separately at the correlation* of 1st, 2nd, or 3rd period shots-for percentages to final goals-for percentages. The result above clearly shows that the 3rd period SF% begins to drop away drastically after 1977 or so, after a quarter-century of running pretty close to the others. It does seem possible, then, that the re-introduction of overtime in 1983-84 (gone since 1943-44) had an impact on the growth of score effects (although I’m not sure how); on the other hand, the introduction of the “loser point” in 1999-2000 doesn’t seem to have had any effect. We can also do a similar graph of correlations to goals-for percentage to validate the use of 2pS%:

As you can see, score effects have essentially become the norm, much to the detriment of overall shot differential. At any rate, whomever put two-and-two together back in the 1970s probably had the right idea; I’d forward the hypothesis that the 1970s NHL was ripe for change and innovation (a lot of competition; growth of league = increase in decision-makers and opportunities to exploit market inefficiencies). In that kind of environment, protecting the lead quickly became a best practice, and it steadily grew to a league-wide practice by the mid-1990s or so.

* Or a -1.0 to +1.0 relationship of the variance in one variable to the variance in another; positive means as one goes up, the other tends to go up, suggesting a positive relationship or correlation. A negative correlation suggests that, as one goes up, the other tends to go down. The closer to 0.0, the less likely the variables have any relationship at all.

Consistency in the NHL: How often do teams tend to play “their game”

Garret Hohl NHL League-Wide Analysis 4 Comments

Source: Bruce Bennett/Getty Images North America

INTRODUCTION:

Our very first published article used shot attempt differentials to see if certain teams were more consistent than others in their performance. We observed that teams differed greatly in how they performed on average, but not so much in their levels of consistency, as in the spread of their performances.

One of the commentators of the article, under name of “Anthony Delage” wondered if team’s differed much in playing “their game”, or in other words: how often low-event team’s play low-event games vs high event teams play high-event games.

See more after the jump.

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Why NHL Stats and Scouting Must Work Together

Benjamin Wendorf Theory Tags: , , , , , , , Leave a comment

File:2006nhldraft-stage.jpg

Photo by Arnold C, via Wikimedia Commons

I think it’s fair to say that people familiar with hockey scouting and stats analysis know that there is a bit of a rift between the two (not unlike what exists in baseball). The former, as in baseball, has a long history as the standard in hockey analysis, being at-or-near the forefront of drafting, trading, and free agency decisions for teams. The latter is expanding its reach exponentially into league offices, and has many a pro-stats person questioning the abilities of scouts to analyze players (and vice versa). There are at least preliminary attempts to reach out, on the part of Corey Pronman at Hockey Prospectus (and ESPN), but scouting and stats analysis both have a lexicon, methods, and best practices, and devotees of one probably don’t have much time to develop proficiency in the other.

Yet, therein lies a problem and a solution. There is a common thread between these two groups, the desire to usefully analyze hockey players. They each have their own approach, but neither necessarily contain such complicated concepts that they cannot be read by a conscientious analyst. But most importantly, they have something to offer one another that could improve both areas of analysis.
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An Introduction to Analyzing Neutral Zone Data (with Graphs)

garik16 Neutral Zone Analysis 3 Comments

Goons like Eric Boulton tend to break Graphs featuring Neutral Zone Data in very……..not-good ways.

A little over 2 years ago, Eric T. introduced us to the idea of tracking play in the neutral zone, in the form of tracking “Zone Entries.”   Zone Entry Tracking is now being done by trackers for a few different teams (Isles, Canes, Ducks, Kings, Sharks, Flyers, Caps, and more here and there) although not most teams’ data has not been collected yet such that people can compare players across teams.

That’s something I’d like to change, so I will be using this space to acquire data from various teams and compare teams and players.  That said, I’d like to explain how we analyze neutral zone data in the first place.

Most Neutral Zone trackers track using a spreadsheet created by Eric, which collects the time, player, and type of each entry.  That sheet compiles the 5 on 5 and 5 on 5 close individual #s of each player (How many entries, What % of entries were via carry, how many shots per type of entry, etc.) and the team.

Using a tool created by Red Line Station (@Muneebalummcu) and with some help from Eric T, we can then use this data to get on-ice data for every player.  This is to me, the real gold mine of neutral zone data – we can see not just how often a player carries in, but how often the opponents do so against him, and whether opponents are carrying it in or instead dumping.   We can also use this data to determine which players aren’t getting it done once the puck is in the offensive or defensive zones, although how repeatable that data is is still in question (More on this in a bit)

There are a few neutral zone stats I think are most worth highlighting: Continue reading →

The Top “Young Guns” in NHL History

Benjamin Wendorf NHL League-Wide Analysis Tags: , , , , , , , , , , , 1 Comment

File:Orr Trip.jpg

Photo by “Djcz” via Wikimedia Commons

I don’t think we engage the idea of the place in history that many of today’s best players hold, and I partly attribute that to the difficulty of finding points of comparison across generations. Simply using raw scoring data doesn’t do the best job because a.) everyone knows Gretzky wins, and b.) we know that scoring fluctuated drastically in the 1980s, and it wasn’t because all the best shooters and passers were playing then. With that in mind, I’ve stewed over ways to bring these different generations together, in such a way that we can be comfortable comparing them. It’s led me to build a couple of metrics that move a little bit away from the counting statistics (G, A, PTS) and towards some metrics that demonstrate a player’s share of their team’s results.

The two metrics I’m focusing on for these young guns both relate to offensive measures, but I think that generally they also allude to a player’s importance to play overall. I tend to agree with Vic Ferrari’s assertion (see his third comment here) that forwards and only a select number of defensemen play much of a role in driving offense, and recalling some of the player types implicated in Steve Burtch’s work over at Pension Plan Puppets on Shut-Down Index, I’d propose that players that drive possession (forwards and defense) more generally will return some signals in regards to shooting or playmaking. Whether that simply means, in the future, we’ll get more from simply looking at passes and shots (or robots will do the whole darn thing and save me the trouble), I can’t say. For now, though, I created %TSh, or percentage of team shots, which expresses the proportion of team shooting a player does (in games they played), and %TA, which does the same exercise with team assists. While the issue of whether this expresses positive possession players is ripe for debate, it’s indisputable that players strong in these metrics will be drivers of offense for their teams.

In that spirit, I wanted to delve into some nifty historical data; I’ve been able to go all the way back to 1967-68 with data on %TSh and %TA, and it returns some fascinating studies on NHL legends vis-à-vis today’s stars. For this piece, I’m focusing on the players that get everyone excited, so-called “young guns,” or players under 25 that have already demonstrated their ability at the top level. How do contemporary young guns measure up all-time?

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Friday Quick Graphs: Shooting and Playmaking Contributions, 1967-68 through 2012-13

Benjamin Wendorf Friday Quick Graphs, NHL Topical Analysis Tags: , , , , , , , Leave a comment

I’ve just finished a pretty massive dataset, so I’m geeking out a bit over what I can do with it. Just the beginning, above…this is the distribution of %TSh (player shots divided by estimated team shots in games they played) and %TA (same equation, but with assists) season performances, 20+ GP, from 1967-68 through 2012-13. Per recent arguments about Ovechkin, I’ve added lines showing where his best season (2008-09) and most recent full season (2012-13) fall on the list; his current season would fall approximately in the same place as last season.

Those of you who’ve been following me on Twitter know that I’ve put together a pretty substantial dataset, and I’ve been working through the data with a metric I’ve used for a while. %TSh is a player’s shots divided by his team’s estimated shot total in games they played (Team Shots / Team GP, multiplied by player GP). The measure gives us an idea of the player’s shooting contribution to the team’s offense. It moves outside the pesky variance of shooting percentage and gets closer to a stable indicator of offensive role. I’ve done the same with %TA, which is the same equation for assists. The reason for estimated team totals is we don’t yet have good macro-data on specific games that players played before 1987-88, but the metric runs essentially in lock-step with the real thing and I want to provide a useful, historical point of comparison. Doing this allows us to look 20 years further back.

The distribution above includes over 23,000 player seasons over 20 GP; the orange distribution is %TA, and black is %TSh. I used the marks to connect back to the previous week’s bizarre flame war over Ovechkin’s value and approach to the game; the top one shows Ovechkin’s peak year, 2008-09 (20%), which also happens to be the highest %TSh of all-time. The bottom mark is Ovechkin’s 2012-13 (16.3%), which I’m using because his current season is just slightly higher – it would be good for 16th best in NHL history.

I also did a second graph, wanting to look at the relationship of %TSh to %TA, to see just how much they ran together:

Related to the previous post, I decided to see if the relationship between TSh% and %TA was too close to tell me anything. %TSh is on the x-axis, and %TA is on the y. As you can see, they do run together, which is okay, because rebounds can result in assists for the shooter, and players with a lot of shots will generally be engaged in the offense in all ways. That being said, it’s not so close that they aren’t distinctive. The plot above does look scattered enough for these two metrics to tell us something apart from one another.

In the graph above, the x-axis is %TSh, and the y-axis %TA. Intuitively, these run together a fair amount, as shots create rebounds that can be counted as assists, and a player that shoots a lot is likely to be more heavily involved in the entire offense. That said, they don’t run nearly so close together as to render either measure moot. I think %TA can be a valuable counter-weight for assessing defensemen. Anyway, this is the tip of an enormous iceberg of data, so don’t be surprised to see me refer to and use %TSh and %TA again.

Input versus Output: An Ongoing Battle that No One Knows About

Garret Hohl NHL Topical Analysis Tags: , , , , , , , 1 Comment

http://xkcd.com/904/

XKCD comics is written by Randall Munroe, a physicist who probably doesn’t know what  hockey underlying numbers (ie: #fancystats or advance statistics) even are, let alone supports them… yet – for the most part – he gets it.

Mainstream sports commentary is full of poor analysis when it comes to using numbers appropriately. Most of this comes from a lack of understanding between the difference between inputs versus outputs and how much a player can control certain factors. (It should be noted that this is a broad generalization; not everyone falls into this category).

Benjamin Wendorf displayed a bit of these factoids in his recent article Why The Hockey News’ Ken Campbell is Wrong About Alex Ovechkin, but Campbell still didn’t get it.

What happened:

For those that do not know, here is a quick summary of Campbell’s article:
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Outperforming PDO: Mirages and Oases in the NHL

Benjamin Wendorf NHL League-Wide Analysis, Theory Tags: , , , , , , , , , , , 3 Comments

Above is the progressive stabilization (game-by-game, cumulatively) of all-situations PDO over time for the 30 NHL teams. It’s a demonstration of the pull of PDO towards the average (1000, or the addition of team SV% and shooting percentage with decimals removed), and it gives you a sense of the end game: an actual spread of PDO, from roughly 975 to roughly 1025. In other words, if you were just to use this data, you could probably conclude that it’s not outside expectations for a team to outperform 1000 by about 25 (or 2.5%) on either side.

That’s all well and good, but PDO is a breakdown of two very different things, a team’s shooting and goaltending, two variables that understandably have very little to do with each other (they are slightly related because rink counting bias usually affects both). Shooting percentage can hinge on a number of contextual variables, though its reliance on a team’s player population usually can bring it a bit in-line with league averages. Save percentage, on the other hand, hinges on one player, and what’s more past performances suggest that a single goaltender can quite significantly outperform expectations. In this piece, I want to jump into the sliding variables of PDO, and what we can expect from teams, but first I want to begin with why I’m working with all-situations PDO.

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